Glossary

0-9

1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network 0-shot learning

A

AGI / Artificial General Intelligence Artificial Intelligence (AI)Attention Algorithm Autoencoder

B

BERT Boosting Bias Backpropagation Batch Normalization

C

Cross-Validation Classifier / Classification CNN / Convolutional Neural Network Chatbot Clustering

D

Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

Forward Propagation Foundation Model Fine-tuning Feature Extraction Fusion / Multimodal Fusion

G

GAN / Generative Adversarial Network Grounding Graph Neural Network (GNN)Gradient Descent Generative AI

H

Hierarchical Model Hallucination Heuristic Hyperparameter Hidden Layer

I

Instruction tuning Imbalanced Data Instance / Sample Intelligence Amplification / Augmentation Interpretability

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

K-Shot Learning Kernel Trick KL Divergence (Kullback–Leibler Divergence)K-means Clustering Knowledge Distillation

L

LSTM / Long Short-Term Memory Large Language Model (LLM)Learning Rate Loss Function Latent Variable

M

Meta-learning Multimodal / Multimodality Multi-head Attention Model Machine Learning (ML)

N

NLU / Natural Language Understanding Novelty Detection / Anomaly Detection Normalization Neural Network NLP / Natural Language Processing

O

One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

Q-learning Quality Estimation Query Quantization Queue / Buffer

R

RNN / Recurrent Neural Network Reinforcement Learning (RL)Retrieval Augmented Generation (RAG)Regularization Representation Learning

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

Vision Transformer (ViT)Vector Embedding Vanishing / Exploding Gradient Variational Autoencoder (VAE)Validation Set

W

Weight Decay Workflow Weak Supervision Whitening / Whitening Transformation Word Embedding

X

X-axis / feature axis XLM XLNet XAI / Explainable AI XOR problem

Y

YAGNI (You Aren't Gonna Need It)Yoga of AI Y-transform / YUV Y-axis / feature axis Yield (model yield / throughput)

Z

Z-score Normalization Zygosity in augmentation Zero-centric / Zero-bias initialization Zero-gradient phenomenon Zero-shot Learning / Zero-shot inference

什麼是正則化

正則化是一種用於統計建模和機器學習的技術，旨在防止過擬合。過擬合是指模型在訓練數據上表現良好，但在新數據上卻無法泛化，導致預測不準確。通過引入額外的約束或懲罰項，正則化有助於簡化模型，提高其在未見數據上的表現。

一方面，正則化通過增加一個懲罰項（如 L1 或 L2 範數）來抑制複雜模型的影響，促使模型學習更簡單的結構，這通常能提高模型的泛化能力。常見的正則化方法包括嶺迴歸（L2 正則化）和套索迴歸（L1 正則化）。這些方法在許多實際應用中都表現出色，例如在圖像識別和自然語言處理的任務中。

另一方面，雖然正則化有助於改善模型的穩定性和預測能力，但它也可能導致信息的丟失，尤其是在數據量較小的情況下。此外，選擇合適的正則化參數也是一項挑戰，過強的正則化可能會導致欠擬合。

未來，隨著數據集的不斷擴大和計算能力的提升，正則化技術也在不斷演化。例如，新的正則化方法如 dropout 和 batch normalization 等正逐漸被廣泛接受，顯示出在深度學習中的重要性。總的來說，正則化是構建高效且穩健模型的關鍵手段，其重要性在機器學習的發展中只會愈加突出。